They want something like the Star Trek computer or one of Tony Stark's AIs that were basically deus ex machinas for solving some hard problem behind the scenes. Then it can say "model solved" or they can show a test simulation where the ship doesn't explode (or sometimes a test where it only has an 85% chance of exploding when it used to be 100%, at which point human intuition comes in and saves the day by suddenly being better than the AI again and threads that 15% needle or maybe abducts the captain to go have lizard babies with).

AIs that are smarter than us but for some reason don't replace or even really join us (Vision being an exception to the 2nd, and Ultron trying to be an exception to the 1st).

yes you can enumerate all inputs, because thoy are not continuous. You just raise the finite number of different tokens to the finite context size and that's exactly the size of the table you would need. finite*finite=finite. You are describing training, i.e how the function is geerated. Yes correlations are found there and encoded in a couple of matrices. Those matrices are what are used in the llm and none of what you said applies. Inference is purely a markov chain by definition.

They don’t want AI, they want an app.

You can say that the whole system is deterministic and finite, so you could record every input-output pair. But you could do that for any program. That doesn't make every deterministic function a Markov process. It just means it is representable in a finite way. The question is not whether the function can be stored. The question is whether its behavior matches the structure and assumptions of a Markov model. In the case of LLMs, it does not.

Inference does not become a Markov chain simply because it returns a distribution based on current input. It becomes a sequence of deep functional computations where attention mechanisms simulate hierarchical, relational, and positional understanding of language. That does not align with the definition or behavior of a Markov model, even if both map a state to a probability distribution. The structure of the computation, not just the input-output determinism, is what matters.

"Artificial intelligence refers to computer systems that can perform complex tasks normally done by human-reasoning, decision making, creating, etc.

There is no single, simple definition of artificial intelligence because AI tools are capable of a wide range of tasks and outputs, but NASA follows the definition of AI found within EO 13960, which references Section 238(g) of the National Defense Authorization Act of 2019.

• Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets.
• An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action.
• An artificial system designed to think or act like a human, including cognitive architectures and neural networks.
• A set of techniques, including machine learning that is designed to approximate a cognitive task.
• An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting."

This is from NASA (emphasis mine). https://www.nasa.gov/what-is-artificial-intelligence/

The problem is that you are reading the word intelligence and thinking it means the system itself needs to be intelligent, when it only needs to be doing things that we would normally attribute to intelligence. Computer vision is AI, but a software that detects a car inside a picture and draws a box around it isn't intelligent. It is still considered AI and has been considered AI for the past three decades.

Now show me your blog post that told you that AI isnt AI because it isn't thinking.

This paper does provide a solid proof by counterexample of reasoning not occuring (following an algorithm) when it should.

The paper doesn't need to prove that reasoning never has or will occur. It's only demonstrates that current claims of AI reasoning are overhyped.

You'd think the M in LLM would give it away.

I think it's an easy mistake to confuse sentience and intelligence. It happens in Hollywood all the time - "Skynet began learning at a geometric rate, on July 23 2004 it became self-aware" yadda yadda

But that's not how sentience works. We don't have to be as intelligent as Skynet supposedly was in order to be sentient. We don't start our lives as unthinking robots, and then one day - once we've finally got a handle on calculus or a deep enough understanding of the causes of the fall of the Roman empire - we suddenly blink into consciousness. On the contrary, even the stupidest humans are accepted as being sentient. Even a young child, not yet able to walk or do anything more than vomit on their parents' new sofa, is considered as a conscious individual.

So there is no reason to think that AI - whenever it should be achieved, if ever - will be conscious any more than the dumb computers that precede it.

In case you haven't seen it, the paper is here - https://machinelearning.apple.com/research/illusion-of-thinking (PDF linked on the left).

The puzzles the researchers have chosen are spatial and logical reasoning puzzles - so certainly not the natural domain of LLMs. The paper doesn't unfortunately give a clear definition of reasoning, I think I might surmise it as "analysing a scenario and extracting rules that allow you to achieve a desired outcome".

They also don't provide the prompts they use - not even for the cases where they say they provide the algorithm in the prompt, which makes that aspect less convincing to me.

What I did find noteworthy was how the models were able to provide around 100 steps correctly for larger Tower of Hanoi problems, but only 4 or 5 correct steps for larger River Crossing problems. I think the River Crossing problem is like the one where you have a boatman who wants to get a fox, a chicken and a bag of rice across a river, but can only take two in his boat at one time? In any case, the researchers suggest that this could be because there will be plenty of examples of Towers of Hanoi with larger numbers of disks, while not so many examples of the River Crossing with a lot more than the typical number of items being ferried across. This being more evidence that the LLMs (and LRMs) are merely recalling examples they've seen, rather than genuinely working them out.

I'd encourage you to research more about this space and learn more.

As it is, the statement "Markov chains are still the basis of inference" doesn't make sense, because markov chains are a separate thing. You might be thinking of Markov decision processes, which is used in training RL agents, but that's also unrelated because these models are not RL agents, they're supervised learning agents. And even if they were RL agents, the MDP describes the training environment, not the model itself, so it's not really used for inference.

I mean this just as an invitation to learn more, and not pushback for raising concerns. Many in the research community would be more than happy to welcome you into it. The world needs more people who are skeptical of AI doing research in this field.

no, not any computer program is a markov chain. only those that depend only on the current state and ignore prior history. Which fits llms perfectly.

Those sophisticated methods you talk about are just a couple of matrix multiplications. Those matrices are what's learned. Anything sophisticated happens during training. Inference is so not sophisticated. sjusm mulmiplying some matrices together and taking the rightmost column of the result. That's it.

Yes, LLM inference consists of deterministic matrix multiplications applied to the current context. But that simplicity in operations does not make it equivalent to a Markov chain. The definition of a Markov process requires that the next output depends only on the current state. You’re assuming that the LLM’s “state” is its current context window. But in an LLM, this “state” is not discrete. It is a structured, deeply encoded set of vectors shaped by non-linear transformations across layers. The state is not just the visible tokens—it is the full set of learned representations computed from them.

A Markov chain transitions between discrete, enumerable states with fixed transition probabilities. LLMs instead apply a learned function over a high-dimensional, continuous input space, producing outputs by computing context-sensitive interactions. These interactions allow generalization and compositionality, not just selection among known paths.

The fact that inference uses fixed weights does not mean it reduces to a transition table. The output is computed by composing multiple learned projections, attention mechanisms, and feedforward layers that operate in ways no Markov chain ever has. You can’t describe an attention head with a transition matrix. You can’t reduce positional encoding or attention-weighted context mixing into state transitions. These are structured transformations, not symbolic transitions.

You can describe any deterministic process as a function, but not all deterministic functions are Markovian. What makes a process Markov is not just forgetting prior history. It is having a fixed, memoryless probabilistic structure where transitions depend only on a defined discrete state. LLMs don’t transition between states in this sense. They recompute probability distributions from scratch each step, based on context-rich, continuous-valued encodings. That is not a Markov process. It’s a stateless function approximator conditioned on a window, built to generalize across unseen input patterns.